Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence. (September 2020)

• Record Type: Journal Article
• Authors: Abdulridha, Jaafar; Ampatzidis, Yiannis; Roberts, Pamela; Kakarla, Sri Charan
• Is Part Of: Biosystems engineering. Volume 197(2020)
• Page Start: 135
• Page End: 148
• Publication Date: 2020-09
• Subjects: Disease detection -- Vegetation indices -- Remote sensing -- Machine learning; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2020.07.001
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : In this study hyperspectral imaging (380–1020 nm) and machine learning were utilised to develop a technique for detecting different disease development stages (asymptomatic, early, intermediate, and late disease stage) of powdery mildew (PM) in squash. Data were collected in the laboratory as well as in the field using an unmanned aerial vehicle (UAV). Radial basis function (RBF) was used to discriminate between healthy and diseased plants, and to classify the severity level (disease stage) of a plant; the most significant bands to differentiate between healthy and different stages of disease development were selected (388 nm, 591 nm, 646 nm, 975 nm, and 1012 nm). Furthermore, 29 spectral vegetation indices (VIs) were tested and evaluated for their ability to detect and classify healthy and PM-infected plants; the M value was used to evaluate the VIs. The water index (WI) and the photochemical reflectance index (PRI) were able to accurately detect and classify PM in asymptomatic, early, and late development stages under laboratory conditions. Under field conditions (UAV-based), the spectral ratio of 761 (SR761) accurately detected PM in early stages, and the chlorophyll index green (CI green), the normalised difference of 750/705 (ND 750/705), the green normalised difference vegetation index (GNDVI), and the spectral ratio of 850 (SR850) in late stages. The classification results, by using RBF, in laboratory conditions for the asymptomatic and late stage was 82% and 99% respectively, while in field conditions it was 89% and 96% in early and late disease development stages, respectively. Highlights: A lab- and UAV-based powdery mildew (PM) disease detection system was developed. Spectral reflectance analysis of PM-infected squash leaves was performed. The most significant wavelengths and vegetation indices were selected. The technique was able to detect different stages of PM progress in squash.